As a junior full-stack developer, I took on the Brimble task with just three days to build a one-page platform that deploys apps from a Git repository and streams live logs—all running with a single docker compose up command. Despite power outages and unfamiliar tools like Railpack and Caddy, this challenge pushed me beyond normal coding into real-time production debugging. Below, I answer the most common questions about this experience, from the core requirements to the lessons I learned.
What is the Brimble task and what does it involve?
The Brimble task is a hands-on challenge where you build a complete system that can deploy applications from a Git repository and stream build and runtime logs in real time to a web interface. The entire system must be startable on any clean machine using a single docker compose up command. The backend handles cloning the repository, building the application (using tools like Railpack), and running it. Logs from each step are streamed to the frontend via Server-Sent Events (SSE) or WebSocket. Additionally, a reverse proxy like Caddy is configured to route traffic to multiple deployments under unique paths without conflicts. This goes beyond typical CRUD apps—it requires integrating version control, containerization, live streaming, and routing in one seamless pipeline.
What were the main challenges faced during the Brimble task?
The task introduced three hard requirements that stretched typical development skills. First, the entire system must work with just docker compose up on a clean machine—no manual steps or extra configurations. This forced me to think about reproducibility from day one. Second, live log streaming over SSE or WebSocket required real-time communication between the backend build processes and the frontend. Third, Caddy had to serve multiple deployments under unique subpaths (like /deploy/abc123) while avoiding routing conflicts. During implementation, I hit configuration errors, build failures due to Railpack settings, and timing issues where logs didn't stream until after the build finished. Each challenge demanded systematic debugging—checking network logs, inspecting container outputs, and tweaking Caddyfile rules. Overcoming them meant shifting from feature-building to production-level troubleshooting.
Why is the Brimble task significant for a developer's growth?
The Brimble task matters because it forces you to think like an engineer working on real production systems, not just a coder writing features. It tests your ability to integrate multiple technologies seamlessly, handle edge cases in deployment, and debug under time pressure. For me, it was a chance to move beyond comfortable frameworks and dive into tools like Railpack and Caddy that you don’t encounter in standard tutorials. More importantly, it taught me resilience—when electricity kept cutting out and errors piled up, I had to stay focused and methodical. The task also proves you can build an end-to-end system that behaves consistently across machines, a skill invaluable for DevOps and full-stack roles. Ultimately, completing it gave me the confidence to tackle complex, unfamiliar problems without waiting to know everything upfront.
How did you approach the deployment and debugging process?
I started by developing the backend and frontend separately, ensuring each worked in isolation. The backend handled repository cloning, building with Railpack, and log streaming; the frontend displayed logs and allowed deployment triggers. Once both were stable locally, I connected them into a Docker Compose setup. This is where most errors surfaced—Caddy configuration conflicts, build environment differences, and log stream delays. My debugging workflow was systematic: I checked Docker logs for each service (docker compose logs), inspected Caddy’s routing with curl, and added temporary debug output to the backend. For live log streaming, I discovered that the build process needed to emit events asynchronously; I switched from blocking subprocess calls to using Python’s asyncio.create_subprocess_exec with streaming. Each fix was tested by rebuilding the stack from scratch to ensure the single-command experience still worked.
What is the system architecture and which core components were used?
The architecture centered around three main services orchestrated by Docker Compose. The backend (built with Python and FastAPI) handled repository cloning via GitPython, building using Railpack (a universal build tool similar to Nixpacks), and running the deployed app. It streamed logs using Server-Sent Events over a persistent endpoint. The frontend (a simple React app) connected to the backend’s SSE endpoint and rendered logs in a terminal-like UI. The Caddy reverse proxy was configured to route requests to the appropriate backend service based on deployment IDs, ensuring each app’s traffic went to its correct container. Railpack played a key role by auto-detecting the project language and generating a Dockerfile, which the backend then built. All services communicated over an internal Docker network, and everything started with a single docker compose up command.
Can you explain the request-response flow of the platform?
Here’s how the flow works step by step:
- A user submits a Git repository URL via the frontend form.
- The frontend sends a POST request to the backend API endpoint
/deploy. - The backend immediately returns a deployment ID and starts the process asynchronously.
- The frontend opens an SSE connection to
/stream/{deployment_id}to listen for logs. - Backend clones the repo, uses Railpack to build the app, and then runs it in a separate container.
- All build and runtime logs are emitted as SSE events, which the frontend displays in real time.
- Once deployed, the backend creates a Caddy route so the app is accessible at
/deploy/{id}. - Any request to that route is proxied by Caddy to the running app container.
This flow ensures that users see progress live and can access their deployed application immediately after logs finish.
What lessons did you learn from this project?
The biggest lesson was that starting before you feel ready is more important than knowing every detail. I didn’t know Railpack or Caddy before this task, but hands-on experimentation with real errors taught me faster than reading documentation. I also learned the value of breaking down a monolithic problem into isolated parts—backend, frontend, infrastructure—and only integrating once each part was solid. Debugging under time pressure improved my ability to read logs systematically and guess the most likely cause first. Finally, I realized that streaming real-time data requires careful handling of asynchronous processes and connection management. This project gave me the courage to tackle future production systems with a structured, resilient mindset.